A fully connected (FC) layer connects every neuron in the lower layer to every neuron in the upper/next layer. Mostly, they are used as the last few layers of a model usually after convolution/pooling layers. FC takes the output of the previous layer and assigns weights to predict the probabilities for class labels. Due to a large number of connections, a large number of parameters need to be adjusted which significantly increases the computational overhead. Moreover, due to a large number of parameters, the model becomes more sensitive to overfitting [1]}. However, to mitigate the effect of overfitting, a dropout method is introduced in [2]}.
